摘要
提出一种基于加权图模型的手指静脉网络特征描述方法。对于一幅手指静脉图像,通过图像划分获得图的顶点集,利用三角剖分获得图的边集,边的权重由边所连接顶点之间的特征相似度决定。通过这种方式,一幅手指静脉图像可转化为一个加权图,并通过度量加权图邻接矩阵之间的相似度实现手指静脉识别。详细研究影响识别结果的几个因素,并通过试验证明了该方法的有效性。
A new weighted graph construction method was proposed for finger-vein network representation. For a weighted graph,its nodes and edges were respectively generated by dividing image into blocks and a triangulation algorithm,and the weights of edges were valued using the feature similarities between adjacent blocks. In this way,a finger-vein image could be represented by a weighted graph,and the adjacency matrix of this weighted graph was used for finger-vein recognition. The experiment results proved the effectiveness of the method,and some important factors that affected graph recognition results were discussed in detail.
作者
叶子云
杨金锋
YE Ziyun;YANG Jinfeng(College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China;Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China)
出处
《山东大学学报（工学版）》
CAS
北大核心
2018年第3期103-109,共7页
Journal of Shandong University（Engineering Science）
基金
国家自然科学基金资助项目(61379102
U1433120
61502498)
中央高校基本科研业务费专项资金资助项目(3122017001)
关键词
手指静脉识别
加权图
图论
特征提取
finger-vein recognition
weighted graph structure
graph theory
feature extraction
